Takano et al. BMC Medical Genetics(2020) 21:205
https://doi.org/10.1186/s12881-020-01142-7

RESEARCH ARTICLE

Open Access

Effect of DNMT3A polymorphisms on CpG
island hypermethylation in gastric mucosa
Hikaru Takano1, Tomoyuki Shibata2, Masakatsu Nakamura1, Naoko Sakurai1, Tasuku Hayashi1, Masafumi Ota1,
Tomoe Nomura-Horita1, Ranji Hayashi1, Takeo Shimasaki1, Toshimi Otsuka1, Tomomitsu Tahara3 andTomiyasu Arisawa1*

Abstract
Background: CpG methylation of tumor suppressor genes occurs in the early stage of carcinogenesis. Detecting
risk factors for aberrant CpG methylation is clinically important for predicting cancer development. DNA
methyltransferase (DNMT) 3a is considered to play critical roles in the DNA methylation process duringpathogenesis. In this study, we evaluated the association between DNMT3A polymorphisms (rs6733868 andrs13428812) and CpG methylation status in non-cancerous gastric mucosa.
Methods: We determined the DNMT3A genotype and CpG methylation status of 4 genes (p14ARF, p16INK4a, DAPK,
and CDH1) in 510 subjects without gastric cancer. Helicobacter pylori (HP) infection status was determined by the
rapid urease test, urea breath test, speculum examination, or serum antibody test. We determined the DNMT3A
genotype using polymerase chain reaction single-strand conformation polymorphism (PCR-SSCP). CpG methylation
status was determined by methylation-specific polymerase chain reaction (MSP). When the methylated band wasstronger than 10 ng/μL according to the DNA marker, we judged CpG island hypermethylation (CIHM) to bepresent. Associations between genotypes and susceptibilities were assessed by logistic regression analysis.
Results: The minor allele frequencies of both polymorphisms (rs6733868 and rs13428812) were lower in the CpG
methylated groups of each of the 4 genes (p14ARF, p16INK4a, DAPK, and CDH1). Using a dominant genetic model,
rs6733868 was significantly associated with the hypermethylation of each gene, whereas rs13428812 was associatedwith the methylation of 3 genes (all except p14ARF). When low-CIHM was defined as 1 or 2 CpG islands methylated
and high-CIHM was defined as 3 or more CpG islands methylated, carrying the minor allele of rs6733868 was
associated with both decreased low- and high-CIHM, and that of rs13428812 also was associated with a decrease.
Comparing low-CIHM with high-CIHM, carrying the minor alleles of rs6733868 or rs13428812 was related todecreased susceptibility to high-CIHM. In HP-infected subjects, carrying the minor alleles of rs6733868 or rs13428812
had a significantly greater association with decreased susceptibility to high-CIHM.
Conclusions: Our study indicates that polymorphisms of DNMT3A are associated with the accumulation of gene
methylation in gastric mucosa. Carrying the minor alleles of rs6733868 or rs13428812 inhibits aberrant gene
methylations, which are typically enhanced by HP infection.
Keywords: DNMT3A, Genetic polymorphism, CpG island, Hypermethylation, Gastric mucosa* Correspondence: tarisawa@kanazawa-med.ac.jp
1
Department of Gastroenterology, Kanazawa Medical University, 1-1 Daigaku,
Uchinada-machi, Ishikawa 920-0293, Japan
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Takano et al. BMC Medical Genetics(2020) 21:205

Background
Gastric cancer is one of the most common and deadly
malignancies in the world. In 2018, gastric cancer was
ranked as the fifth most commonly diagnosed cancer
globally, and gastric cancer deaths accounted for 8.2% of
all cancer deaths. Gastric cancer is thus the third leading
cause of death from cancer. Additionally, more than 50%
of gastric cancers occur in eastern Asia [1]. Despite thedeclining trend in Japan due to insurance coverage toeradicate Helicobacter pylori (HP), gastric cancer remains a clinically significant malignancy, affecting 50,
000 people annually. It has long been known that HP infection is a risk factor for gastric cancer [2, 3], and it has
been suggested that three steps are involved in gastriccancer progression: HP infection, the development of aprecancerous state of the stomach, and carcinogenesis[4]. Correa proposed an oncogenic sequence in which
differentiated gastric cancer (intestinal type) develops
from HP infection through chronic gastritis (atrophic
gastritis, intestinal epithelialization) [5]. Therefore, it has
been recognized that advanced atrophic gastritis and intestinal epithelialization are essential conditions of the
precancerous stage of gastric cancer from both morphological and histological perspectives [6].
From a molecular biological perspective, however, Maekita et al. found that the accumulation of abnormal DNA
methylation in the gastric mucosa is critical for the precancerous state [7]. We also obtained the same results [8].
Methylation at CpG islands is a key mechanism of gene silencing, and there are known aberrant methylations occurring in specific genes in a variety of cancers, including
gastric cancer [9, 10]. Among the three known DNA
methyltransferases (DNMTs), DNMT3a and DNMT3b
are de novo DNMTs and are critical enzymes that cause
dynamic DNA methylation during embryogenesis andpathogenesis [11]. In addition, overexpression of DNMTs
has been observed in gastric carcinoma and nonneoplastic tissues susceptible to gastric carcinoma [12].
These facts suggest that CpG island hypermethylations(CIHM) caused by DNMTs might be crucial in the development of gastric cancer.
While HP infection is involved in the development ofgastric cancer, the genetic constitution of an individual
might also be involved, as gastric cancer does not occur
in all HP-infected individuals. El-Omar and coworkers
were the first to report that a genetic polymorphism ofinterleukin-1β is implicated in gastric cancer [13]. In ourprevious study, we demonstrated a relationship between
gene polymorphisms of specific genes and gastric cancer susceptibility [14, 15]. Despite the involvement of rs1550117, a
known representative polymorphism of DNMT3A reported
in various carcinomas, its role in gastric cancer is still controversial, particularly in Japanese patients [16]. Recently, we
found that rs6733868 C > G and rs13428812 A > G ofPage 2 of 9

DNMT3A are involved in HP infection, the progression ofgastric mucosal atrophy, and gastric cancer susceptibility in a
Japanese population [17]. However, the involvement of these
DNMT3A gene polymorphisms in the accumulation of aberrant CpG methylation in the gastric mucosa has not beenclarified.
We sought to elucidate the effects of DNMT3A polymorphisms rs6733868 and rs13428812 in the accumulation of CpG methylation in the gastric mucosa and how
HP infection might impact the process.

Methods
Population samples

The study population comprised 510 subjects without
cancer who attended the Endoscopy Center of Fujita
Health University Hospital from January 2006 to December 2012. In all of the study participants, an uppergastrointestinal endoscopy was performed as a part of ahealth check-up, as a secondary examination following
barium X-ray gastrography, or for any symptoms of abdominal discomfort. Of these, 402 were subjects recruited from our previous research [18]. Our exclusion
criteria included: subjects with severe systemic diseases
or malignancies of the stomach or other organs and any
participant with a history of abdominal surgery or HP
eradication. The study protocol was approved by the
Ethics Committee of Fujita Health University School of
Medicine, Japan, and written informed consent was obtained from all participants.
For each subject, biopsy samples were taken from theantrum at the time of endoscopy, and one part of each
was immediately frozen and stored at − 80 °C until use.
Peripheral blood was collected at the time of endoscopy,
and serum was prepared and frozen at − 80 °C. HP infection was determined when at least one of the following
tests was positive: 1) rapid urease test, 2) urea breath
test, 3) speculum examination, or 4) serum antibody test.
Two tumor suppressor genes with methylation associated with aging and HP infection (p14ARF and p16INK4a,
respectively), death-associated protein kinase (DAPK),
and E-cadherin (CDH1) were selected as candidates forthe evaluation of CIHM [7, 17, 18]. Both p14ARF and
p16INK4a are translated from CDKN2A by alternative
splicing [19, 20]. These four genes were selected because
increased CpG island hypermethylation in these genes in
non-neoplastic gastric mucosa has been shown to correlate with a higher risk of gastric cancer [21].
Genotyping

Genomic DNA was extracted from a portion of the frozen
samples using proteinase K. In 408 cases, genomic DNA
was extracted from blood samples. Genotyping was performed by the previously described polymerase chain reaction (PCR)-single-strand conformation polymorphism

Takano et al. BMC Medical Genetics(2020) 21:205(SSCP) method [22]. The following primer sets were used:
for rs6733868: forward, 5′-ctagctagcgggagtcgctgtc-3′ andreverse, 5′-ctcctggctgtgaagcggaag-3′; for rs13428812: forward, 5′-ccccatcatgtcagataccctctg-3′ and reverse, 5′ccttcctaggggacacccttctatt-3′.
PCR was performed using EX Taq HS (Takara Bio,
Shiga, Japan), adding 0.1 μg of genomic DNA to 20 μL of
buffer, denaturing at 95 °C for 3 min, followed by 35 cycles of 15 s at 96 °C, 30 s at 61 °C, and 30 s at 72 °C, anda 5-min final extension at 72 °C. The same PCR conditions were used for rs6733868 and rs13428812. Then,
2 μL of the PCR product was treated in 10 μL of formamide for 5 min at 90 °C and denatured to single strands.
SSCP was performed with the Gene Phor DNA separation system using the Gene Gel Excel 12.5/24 kit (GEHealth Care Bio-Sciences AB, Stockholm, Sweden) at aconstant temperature of 18 °C, and the denatured bands
were detected using a DNA silver staining kit (GEHealth Care Bio-Sciences AB).

Page 3 of 9

size marker (10 ng/μL: 100 bp DNA Ladder; Takara Bio),regardless of the presence of unmethylated bands [23].
Statistical analysis

Hardy-Weinberg equilibria were assessed by the χ2 test.
Mean age was expressed as mean ± SD and analyzed
using Student’s t test. HP infection rate and sex ratio
were compared using Fisher’s exact test. Genotype distribution and allele frequency were also assessed using
Fisher’s exact test. Odds ratios (OR) and 95% confidence
intervals (CI) for the extent of genotype involvement in
DNA methylation were calculated using logistic regression analysis adjusted for sex, age, and HP infection status. The relationship between the number of genes with
CpG methylation and genotype or HP positivity wasassessed by ANCOVA. All analyses were considered significant with p < 0.05. Stata software (version 13; StataCorp LP, College Station, TX, USA) was used forstatistical processing.

Results
Bisulfite reaction and methylation-specific PCR (MSP)
methods

Characteristics of the subjects and allelic frequencies ineach methylated population

To detect DNA methylation, genomic DNA extracted
from biopsy tissues was treated with sodium bisulfite
using a BislFast DNA Modification Kit for Methylated
DNA Detection (Toyobo, Osaka, Japan). The methylationspecific PCR (MSP) reaction was performed as previously
described [8, 18]. The primer sets used are shown in
Table 1. Using EX Taq HS (Takara Bio, Shiga, Japan),
PCR was performed for 0.1 μg of bisulfite-modified DNA
in 20 μL of buffer with an initial denaturing step of 5 min
at 95 °C, followed by 33–35 cycles of 30 s denaturing at
95 °C, 1 min annealing at 57–69 °C, and 1 min extension
at 72 °C, and a final 5-min extension step at 72 °C. 2.5%
agarose gel electrophoresis was performed using 10 μL of
PCR product, stained with ethidium bromide, and visualized by UV illumination. The presence of CIHM wasjudged when the signal of the electrophoresis-separated
positively-methylated band was stronger than that of the

The background and distribution of genotypes of thesubjects, including CpG island methylation status ofeach of the four genes, are shown in Table 2. In the 510
subjects, the distribution for the DNMT3A variants was
as follows: rs6733868: CC = 212, CG = 224, and GG = 74;
rs13428812: AA = 313, AG = 166, and GG = 31, both
meeting Hardy-Weinberg equilibrium (p = 0.25 and p =0.16, respectively), and these distributions did not differ
from the data reported by HapMap-JPT (p = 0.51 and
p = 0.20, respectively). The CpG methylated subjects forthe genes (p14ARF, p16INK4a, DAPK, and CDH1) were167, 134, 252, and 192, respectively. The mean age inthe p14ARF-methylated group was significantly higher
than that in the unmethylated group, whereas no significant difference was found in the other three genes. The
male/female ratio was not significantly different among
methylated and unmethylated groups in all four genes.

Table 1 Primer sets used for methylation-specific polymerase chain reaction
gene namemethylated forward

methylated reverse

product size

p14ARF5′-gtgttaaagggcggcgtagc-3’

5′-aaaaccctcactcgcgacga-3’

122 bpINK4ap165′-ttattagagggtggggcggatcgc-3’

5′-gaccccgaaccgcgaccgtaa-3’

150 bpDAPK

5′-ggatagtcggatcgagttaacgtc-3’5′-ccctcccaaacgccga-3’

98 bp

CDH1

5′- ttaggttagagggttatcgcgt-3’5′-taactaaaaattcacctaccgac-3’

115 bpunmethylated forward

unmethylated reverse

p14ARF5′-tttttggtgttaaagggtggtgtagt-3’5′-cacaaaaaccctcactcacaacaa-3’

132 bpp16INK4a5′-ttattagagggtggggtggattgt-3’5′-caaccccaaaccacaaccataa-3’

151 bpDAPK

5′-ggaggatagttggattgagttaatgtt-3’5′-caaatccctcccaaacaccaa-3’

106 bpCDH15′- taattttaggttagagggttattgt-3’5′-cacaaccaatcaacaacaca-3’

97 bp

Takano et al. BMC Medical Genetics(2020) 21:205

Page 4 of 9

Table 2 Characteristics of the subjects and allelic frequency in CpG methylated population of each genes
overallp14ARF-M

p16INK4a-M

DAPK-M

number of sample

510

167

134

252

192

mean age ± SD

60.5 ± 13.7

62.9 ± 13.7a61.2 ± 11.8

61.5 ± 13.6

60.3 ± 13.0

CDH1-M

male: female

297: 213

91: 76

78: 56

150: 102

117: 75

HP infection status

321/510117/167b

111/134c

186/252c

148/192c

CC

212

81d

69e

122f

100g

CG

224

61

49

112c

70

GG

74

25

16

18

(rs6733868 C > G)

22

36.5%

33.2%

30.2%

29.4%

29.7%i

AA

313

111

97j

169k

145c

AG

166

47

28

73

37

G allele freqencyhc(rs13428812 A > G)

GG

31

9

9

10

10

G allele freqency

22.4%

19.5%

17.2%l

18.5%m

14.8%c-M Methylated
a: p = 0.0046, b: p = 0.019, c: p < 0.0001, d: p = 0.028, e: p = 0.0079, f: p = 0.0022, g: p = 0.0002, h: p = 0.015, i: p = 0.0005,
j: p = 0.0027, k: p = 0.011, l: p = 0.017, m: p = 0.0034 vs. unmathylated group of each gene

The ratio of HP positivity was significantly higher in the
methylated group than in the unmethylated group in all
four genes. In both gene polymorphisms, the minor allele
frequency in the methylated groups tended to be lower.
Association between DNMT3A polymorphisms and CpG
hypermethylation of each gene

For the polymorphism rs6733868, the methylation of all
four genes under study (p14ARF, p16INK4a, DAPK, andCDH1) showed significant involvement as revealed byregression analysis using a dominant genetic model adjusted for sex, age, and HP infection status (Table 3). In
addition, a recessive genetic model also showed thesignificant involvement of DAPK methylation. For
the polymorphism rs13428812, the dominant genetic model showed a significant association withmethylation in three genes, all except p14 ARF. In
contrast, the recessive genetic model showed nosignificant involvement of methylation in any of thefour genes (Table 4).

Table 3 Association between DNMT3A rs6733868 and CpG methylation of each genes
genotype

p14ARF-UM (343)p14ARF-M (167)adjusted OR* (95%CI); p value

CC

131

81

reference

CG

163

61

0.67 (0.46–0.98); 0.038

GG

49

25

1.07 (0.629–1.82); 0.80

genotypep16INK4a-UM (376)p16INK4a-M (134)adjusted OR* (95%CI); p value

CC

143

69

reference

CG

175

49

GG

58

16

0.78 (0.42–1.44); 0.42

DAPK-UM (258)

DAPK-M (252)

adjusted OR* (95%CI); p value

CC

90

122

reference

CG

112

112

GG

56

18

0.28 (0.15–0.49); < 0.0001

genotype

CDH1-UM (318)CDH1-M (192)

adjusted OR* (95%CI); p valuerefernce

112

100

CG

154

70

GG

52

22

-UM: unmethylated; −M: methylated*: adjusted for gender, age and HP infection status

reference
0.60 (0.40–0.91); 0.015

genotypeCC

referencereference
0.59 (0.41–0.85); 0.0050

reference
0.52 (0.36–0.75); 0.0005

0.70 (0.40–1.20); 0.19

Takano et al. BMC Medical Genetics(2020) 21:205

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Table 4 Association between DNMT3A rs13428812 and CpG methylation of each genes
genotype

p14ARF-UM (343)p14ARF-M (167)adjusted OR* (95%CI); p value

AA

202

111

reference

AG

119

47

GG

22

9

genotypep16INK4a-UM (376)p16INK4a-M (134)adjusted OR* (95%CI); p value

AA

216

97

reference

AG

138

28

GG

22

9

0.77 (0.52–1.14); 0.19
0.93 (0.41–2.10); 0.87

0.55 (0.35–0.85); 0.0078

genotype

DAPK-UM (258)

DAPK-M (252)

adjusted OR* (95%CI); p value

144

169

reference

AG

93

73

GG

21

10

genotype

CDH1-UM (318)CDH1-M (192)

adjusted OR* (95%CI); p value

AA

168

145

reference

AG

129

37

21

10

reference

1.44 (0.62–3.37); 0.40

AA

GG

referencereference
0.66 (0.46–0.96); 0.030

0.53 (0.240–1.17); 0.12

reference
0.37 (0.25–0.56); < 0.0001

0.90 (0.40–2.00); 0.79

-UM Unmethylated; −M Methylated*: adjusted for gender, age and HP infection status

Characteristics and allelic frequencies of the subjects by
the number of methylations per gene

The distribution of background and genotype according
to the number of methylations among the four genes werecompared with a reference group (without methylated
genes) (Table 5). The mean age of subjects tended to besignificantly higher in those with methylated genes among
the four genes selected for this study. Also, the rate of HP
infection increased with the number of methylated genes
and was considerably higher for all degrees of methylation.
In addition, HP positivity was significantly correlated with
the increased number of methylated genes (p < 0.0001 by
ANCOVA). For the polymorphism rs6733868, the frequency of the CC genotype was significantly higher in the
methylated group; conversely, the frequency of the GG
genotype was lower, and the frequency of the minor allele
tended to decrease with the number of methylated genes.
Similarly, for the polymorphism rs13428812, the AA
genotype tended to be significantly more frequent in the
methylated group, while the GG genotype was less frequent, and the minor allele frequency tended to decrease
with the number of methylated genes. An inverse correlation between minor allele number for both polymorphisms (rs6733868 and 13,428,812) and the number of
CpG methylated genes was found (Fig. 1).
Association between DNMT3A polymorphisms and thenumber of methylated genes

We performed regression analysis using a dominant genetic
model adjusted for sex, age, and HP infection status in threegroups: a non-CIHM group without methylated genes, alow-CIHM group with one or two methylated genes, and ahigh-CIHM high with three or more methylated genes(Table 6). For the polymorphism rs6733868, carrying theminor allele was significantly associated with a low risk ofmethylation in both the low-CIHM and high-CIHM groups
compared with the non-CIHM group. By comparing thelow- and high-CIHM groups, a significant association wasobserved for carriers of the minor allele with a reduced riskof methylation. Similar results were also obtained with thers13428812 polymorphism.
In 321 (n = 510; 62.9%) HP-infected subjects, no significant association was noted with gene polymorphisms regarding methylation in the low-CIHM and non-CIHM
groups (Table 6). In contrast, there was a strong and significant association in the high-CIHM group, indicating thatcarrying the minor allele for both gene polymorphisms(rs6733868 and rs13428812) was associated with a significant suppression of high-frequency CpG methylation.

Discussion
DNMTs play an important role in DNA methylationand establish methylation patterns on CpG islands.
Among the DNMTs, DNMT3a has a greater effect on denovo methylation than DNMT3b [8]. We previously
demonstrated that DNMT3A polymorphisms (rs6733868
and rs13428812) are associated with the severity of gastric mucosal atrophy, which is accompanied by chronic
inflammation and the subsequent development of gastriccancer [22]. However, whether these polymorphisms
affect CpG island methylation as a precancerous condition has not been revealed. In this study, we examined
whether either gene polymorphism was associated withthe accumulation of methylation of CpG islands in theTakano et al. BMC Medical Genetics(2020) 21:205

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Table 5 Characteristics and allelic frequency of the subjects by number of methylated genes
number of methylated gene

0

1

2

3

number of sample

123

156

131

75

4
25

mean age ± SD

59.8 ± 14.9

59.9 ± 13.3

59.5 ± 14.1

61.9 ± 12.3

68.2 ± 10.3a

male: female

66: 57

100: 56

71: 6046: 2914: 11

HP infection status

53/12389/156b89/131c67/75c23/25c

CC

36

56

61d

44c

15e

CG

57

82

53

24

8

GG

30

18f

17g

7h

(rs6733868 C > G)

j

2

47.6%

37.8%

33.2%

25.3%

24.0%k

AA

57

93l

82m

61c

20n

AG

53

57

43

10

3

GG

13

o

6

6

4

2

G allele frequency

32.1%

22.1%p

21.0%q12.0%c

14.0%rG allele freuqencyic(rs13428812 A > G)

a: p = 0.0082, b: p = 0.016, c: p < 0.0001, d: p = 0.0065, e: p = 0.0051, f: p = 0.0063, g: p = 0.023, h: p = 0.0085,
i: p = 0.025, j: p = 0.0011, k: p = 0.0027, l: p = 0.030, m: p = 0.012, n: p = 0.0021, o: p = 0.032, p: p = 0.0091,
q: p = 0.0048, r: p = 0.010 vs. 0 group

gastric mucosa. The results showed that the minor allele
frequencies of rs6733868 (C > G) and rs13428812 (A >G) were significantly reduced as the number of methylations of the CpG islands of the four genes examined increased. Consistent with the fact that CpG methylation
accumulates during inflammation and aging, we observed an increase in older subjects and with HP infection rates as the number of methylated CpGs increasedin our study population. However, regression analysis
after adjustment for confounding factors showed astrong and significant association of both gene variants,suggesting that DNMT3A gene polymorphism is anindependent factor in the accumulation of methylationof gastric mucosal CpG islands of the studied genes. Although the DNMT3A polymorphism has been reported to
be associated with gastric cancer, HP infection, and gastricmucosal atrophy [16, 22, 23], its association with methylationof gastric mucosa genes has not been clear. Our current
study has revealed this association for the first time.
To assess the degree of CpG island methylation, we selected the CpG sites of four genes (p14ARF, p16INK4a,
DAPK, and CDH1), because we previously reported that
increased CpG island hypermethylation in these fourgenes of the non-neoplastic gastric mucosa correlates

Fig. 1 Correlation of genotypes with the number of genes with CpG methylation. There was a significant inverse correlation between minor
allele number of both polymorphisms (rs6733868 and 13,428,812) and the number of genes with CpG methylation (p < 0.0001 by ANCOVA)

Takano et al. BMC Medical Genetics(2020) 21:205

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Table 6 Association between DNMT3A polymorphisms and number of methylated genes in the whole population and in patients
infected with HP
Whole population

Patients infected with HP
OR (95% C.I.); p value

Allelic variants
rs6733868

CC

CG

GG

OR (95% C.I.); p value

Allelic variants
CC

CG

GG

number of methylated genes
0 (123)

36

57

30

reference

17

22

14

reference

1 or 2 (287)

117

135

35

0.59 (0.37–0.94); p = 0.026

69

89

20

0.74 (0.38–1.42); p = 0.36

3 or 4 (100)

59

32

9

0.34 (0.18–0.64); p < 0.0001

56

26

8

0.26 (0.12–0.55); p = 0.0004

OR (95% C.I.); p value

Allelic variants

Allelic variants
rs13428812

AA

AG

GG

AA

AG

OR (95% C.I.); p value
GG

number of methylated genes
0 (123)

57

53

13

reference

26

21

6

reference

1 or 2 (287)

175

100

12

0.55 (0.36–0.85); p = 0.0073

107

67

4

0.61 (0.32–1.13); p = 0.12

3 or 4 (100)

81

13

6

0.22 (0.11–0.44); p < 0.0001

74

11

5

0.18 (0.083–0.41); p < 0.0001

by logistic regression analysis after adjustment for age, gender and HP infection status(): number of subjects

with a higher risk of gastric cancer [21]. The p14ARF and
p16INK4a proteins are encoded by CDKN2A; these proteins act on the p53 and pRb pathways, respectively, tonegatively regulate the cell cycle [19, 20]. The loss offunction due to methylation or deletion of CDKN2A has
been observed in many cancers [24]. Additionally, theimportance of DAPK and CDH1 in cancer has been revealed [25, 26]. Because these four genes play an important role in carcinogenesis, we could not exclude thepossibility that methylation of these genes renders themsilent, and hence, directly contributes to carcinogenesis.
Nonetheless, since many reports describe CpG methylation of these genes in non-neoplastic areas [18, 21, 27],
it is unlikely that their methylation contributes directly
to carcinogenesis.
Global DNA methylation patterns in human cancer
are altered by hypermethylation of the CpG islands andhypomethylation of the non-CpG parts [28]. The denovo DNMTs have also been implicated in this dynamic
methylation early in tumor development [29]. We presumed that the methylation of these gene groups reflects
the degree of change in global DNA methylation patterns, as they are confirmed to undergo methylationfrom the precancerous lesion stage. In the stomach,
CIHM is associated with HP infection [7, 8, 17], the degree of gastritis [18], and the risk of carcinogenesis [23,27]. Additionally, de novo DNMT expression is more
highly enhanced in tumor and corneal tumor areas thanin non-tumor areas [12]. These previous reports suggestthat HP infection could induce de novo synthesis of
DNMT genes in the stomach, with the subsequent
methylation of CpG islands in genes, which in turn leads
to carcinogenesis. In our current findings, the rate of HP
infection increased with the increasing number of CpG

methylated genes, and a significant relationship between
both rs6733868 and rs13428812 gene polymorphisms
and the number of CpG methylations was found only in
the HP-infected subjects, but not in HP-uninfected subjects (data not shown). Notably, genetic polymorphisms
are not the only regulators of protein expression. These
gene polymorphisms might be of significance when there
is an inducement by HP infection and the induction ofgene expression triggered in DNMTs. However, HP infection does not directly induce DNMT mRNA [27].
Hmadcha et al. reported that the increase in DNMT activity by IL-1β is mediated by reactive oxygen species
and nitric oxide [30]. Thus, it is likely that HP infection
might have induced de novo DNMTs through this system, leading to methylation of CpG. Considering our results that minor alleles carrying the rs6733868 andrs13428812 gene polymorphisms correlated negatively
with the accumulation of CpG methylation, we infer thatthese two types of gene polymorphisms are decreasing
functional types.
There are several clinical limitations in this study.
First, it was a retrospective study using samples collected
at a single institution in Japan. The genetic polymorphisms examined in this study population satisfy the
Hardy-Weinberg equilibrium, and the distribution of the
genotypes is similar to that reported in the HapMapJPT, which indicates that the population distribution istypical of Japanese citizens. However, a follow-up examination at another institution would be necessary. Second, there is no assurance that the validity of the four
selected CpG gene sites studied represents changes in
global DNA methylation patterns. Thus, based on the
methylation status of CpG, the possibility of moremethylation occurring at an earlier stage should beTakano et al. BMC Medical Genetics(2020) 21:205

investigated. Finally, it is unclear how the genetic polymorphisms examined in this study might indeed affectthe expression and function of DNMT3a protein. Fanet al. reported that rs1550117, an A > G variant in the
DNMT3A gene promoter, affects protein expression andelevates DNMT3a expression, leading to the development of gastric cancer [31]. Therefore, we deduced thatthe minor alleles of the gene polymorphisms examinedin this study might be of a hypofunctional type, but confirmation is required at the experimental level.

Conclusions
Our study indicates that the polymorphisms of DNMT3A
are associated with the accumulation of gene methylationin gastric mucosa. Carrying the minor allele of rs6733868
or rs13428812 inhibits aberrant gene methylations, especially under conditions of HP infection.
Abbreviations
HP: Helicobacter pylori; DNMT: DNA methyltransferase; CIHM: CpG island
hyper methylations; DAPK: Death-associated protein kinase; CDH1: E-cadherin;
PCR-SSCP: Polymerase chain reaction-single-strand conformation polymorphism; MSP: Methylation-specific PCR; UV: Ultraviolet; SD: Standard deviation;
OR: Odds ratio; CI: Confidence interval; CDKN2A: Cyclin dependent kinase
inhibitor 2A
Acknowledgments
We would like to thank Prof. Yasuhito Ishigaki for proofreading this manuscript.
Authors’ contributions
TH determined the genotypes, analyzed the data, and wrote the paper. TA
was responsible for supervising the scientific research and writing themanuscript. MN, TO, NS, HT, MO, TN-H, RH, and TS2 contributed to the literature review; data analysis; drafting, editing, and critical revision of the manuscript; and approval of the final version of the manuscript. TT and TS1
obtained the clinical samples and data, and participated in the design of thestudy. All authors have read and approved the final manuscript.
Funding
No funding was received.
Availability of data and materials
All data generated during this study are included in this published article. The
raw data analyzed during the current study are not publicly available due to riskof compromising individual privacy. The application and the written consent
forms state that the data will only be available to the researchers within the
project. For inquires on the data, researchers should first contact the owner ofthe database, Fujita Health University. Please contact the corresponding author
with requests and for assistance with data requests.
Ethics approval and consent to participate
The Ethics Committees of Fujita Health University approved the protocol(HM18–094), and all participants gave their written informed consent.
Consent for publication
Not applicable.
Competing interests
The authors declare that they have no competing interests.
Author details
1
Department of Gastroenterology, Kanazawa Medical University, 1-1 Daigaku,
Uchinada-machi, Ishikawa 920-0293, Japan. 2Department of Gastroenterology,
Fujita Health University, 1-98 Dengakugakubo, Kutsukake-cho, Toyoake
470-1192, Japan. 3Department of Gastroenterology and Hepatology, Kansai
Medical University, 2-5-1 Shin-machi, Hirakata, Osaka 573-1010, Japan.

Page 8 of 9

Received: 29 June 2020 Accepted: 8 October 2020

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